The present paper generalizes curriculum learning for environment generation to multi-agent environments. We use the setup of Teacher-Student Curriculum Learning [1]}, [2]}, [3]}, where an RL-trained teacher samples environments of one or several student agents and is trained alongside the students. The teacher reward is chosen to incentivize the teacher to select environments that maximize student learning. A key challenge of competitive multi-agent environments is that the rewards or success probabilities of students are often not informative about student performance or learning progress: a student might achieve a high reward because they are highly skilled or because their opponents do badly and student rewards may fluctuate widely through skill discovery [4]}.
